Polynomial matrix QR decomposition for the decoding of frequency selective multiple-input multiple-output communication channels

2012 ◽  
Vol 6 (7) ◽  
pp. 704 ◽  
Author(s):  
J. Foster ◽  
J. McWhirter ◽  
S. Lambotharan ◽  
I. Proudler ◽  
M. Davies ◽  
...  
Keyword(s):  
Polynomial Matrix ◽  
Multiple Input Multiple Output ◽  
Communication Channels ◽  
Qr Decomposition ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Frequency Selective

Depth-First QRD-M Detection Algorithm for MIMO Systems

2013 ◽  
Vol 347-350 ◽  
pp. 3478-3481
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han ◽  
Yu Huan Wang
Keyword(s):  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Detection Performance ◽  
Detection Scheme ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Maximum likelihood (ML) detection algorithm for multiple input multiple output (MIMO) systems provided the best bit error rate (BER) performance with heavy calculating complexity. The use of QR decomposition with M-algorithm (QRD-M) had been proposed to provide near-ML detection performance and lower calculating complexity. However, its complexity still grew exponentially with increasing dimension of the transmitted signal. To reduce the problem, an improved detection scheme was proposed here. After constructing the tree detecting model of MIMO systems, the ML search of one layer was done, the branch metrics were calculated and sorted, which gave an ordered set of the layer, then depth-first search were used to search the left layers with termination methods. The proposed algorithm provides near QRD-M detection performance.

scholarly journals A CORDIC based QR Decomposition Technique for MIMO Detection

2018 ◽  
Vol 8 (5) ◽  
pp. 2731
Author(s):  
Shirly Edward A. ◽  
Malarvizhi S.
Keyword(s):  
Complexity Analysis ◽  
Multiple Input Multiple Output ◽  
Qr Decomposition ◽  
Mimo Detection ◽  
Hardware Complexity ◽  
Channel Matrix ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Processing Techniques ◽  
Complex Channel

CORDIC based improved real and complex QR Decomposition (QRD) for channel pre-processing operations in (Multiple-Input Multiple-Output) MIMO detectors are presented in this paper. The proposed design utilizes pipelining and parallel processing techniques and reduces the latency and hardware complexity of the module respectively. Computational complexity analysis report shows the superiority of our module by 16% compared to literature. The implementation results reveal that the proposed QRD takes shorter latency compared to literature. The power consumption of 2x2 real channel matrix and 2x2 complex channel matrix was found to be 12mW and 44mW respectively on the state-of-the-art Xilinx Virtex 5 FPGA.

scholarly journals Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems

2021 ◽  
Vol 11 (16) ◽  
pp. 7305
Author(s):  
Uzokboy Ummatov ◽  
Jin-Sil Park ◽  
Gwang-Jae Jang ◽  
Ju-Dong Lee
Keyword(s):  
Computational Complexity ◽  
Tabu Search ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Qr Decomposition ◽  
Efficient Computation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ordering Schemes

In this study, a low complexity tabu search (TS) algorithm for multiple-input multiple-output (MIMO) systems is proposed. To reduce the computational complexity of the TS algorithm, early neighbor rejection (ENR) and layer ordering schemes are employed. In the proposed ENR-aided TS (ENR-TS) algorithm, the least promising k neighbors are excluded from the neighbor set in each layer, which reduces the computational complexity of neighbor examination in each TS iteration. For efficient computation of the neighbors’ metrics, the ENR scheme can be incorporated into QR decomposition-aided TS (ENR-QR-TS). To further reduce the complexity and improve the performance of the ENR-QR-TS scheme, a layer ordering scheme is employed. The layer ordering scheme determines the order in which layers are detected based on their expected metrics, which reduces the risk of excluding likely neighbors in early layers. The simulation results show that the ENR-TS achieves nearly the same performance as the conventional TS while providing up to 82% complexity reduction.

Improved Stack Algorithm for MIMO Systems

2013 ◽  
Vol 333-335 ◽  
pp. 666-669
Author(s):  
Li Liu ◽  
Jin Kuan Wang ◽  
Xin Song ◽  
Yin Hua Han
Keyword(s):  
Bit Error Rate ◽  
Ordered Set ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
System Capacity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ml Detection

Multiple input multiple output (MIMO) systems could increase wireless communication system capacity enormously. The best optimal detection algorithm for MIMO systems was maximum likelihood (ML) detection algorithm, which could provide the best bit error rate (BER) performance for MIMO systems. However, the computational complexity of ML detection algorithm grew exponentially with the number of transmit antennas and the order of modulation, which resulted in difficult using for practice. A modified MIMO signal detection algorithm which combined ML detection with stack algorithm was presented in this paper. After performing QR decomposition of the channel matrix, the ML detection with length L was done firstly. The partial accumulated metrics were calculated and sorted, which produced an ordered set secondly. Based on the ordered set, stack algorithm was performed to search for the symbol with the minimum accumulated metrics. The proposed algorithm reduced the probability of look back in stack algorithm.

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